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Context is the bottleneck for coding agents now

(runnercode.com)
196 points zmccormick7 | 1 comments | 26 Sep 25 15:06 UTC | HN request time: 0.21s | source
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aliljet ◴[26 Sep 25 15:27 UTC] No.45387614[source]▶
There's a misunderstanding here broadly. Context could be infinite, but the real bottleneck is understanding intent late in a multi-step operation. A human can effectively discard or disregard prior information as the narrow window of focus moves to a new task, LLMs seem incredibly bad at this.

Having more context, but leaving open an inability to effectively focus on the latest task is the real problem.

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bgirard ◴[26 Sep 25 15:34 UTC] No.45387700[source]▶
I think that's the real issue. If the LLM spends a lot of context investigating a bad solution and you redirect it, I notice it has trouble ignoring maybe 10K tokens of bad exploration context against my 10 line of 'No, don't do X, explore Y' instead.
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dingnuts ◴[26 Sep 25 15:47 UTC] No.45387838[source]▶
that's because a next token predictor can't "forget" context. That's just not how it works.

You load the thing up with relevant context and pray that it guides the generation path to the part of the model that represents the information you want and pray that the path of tokens through the model outputs what you want

That's why they have a tendency to go ahead and do things you tell them not to do..

also IDK about you but I hate how much praying has become part of the state of the art here. I didn't get into this career to be a fucking tech priest for the machine god. I will never like these models until they are predictable, which means I will never like them.

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1. moffkalast ◴[26 Sep 25 16:20 UTC] No.45388198[source]▶
That's not how attention works though, it should be perfectly able to figure out which parts are important and which aren't, but the problem is that it doesn't really scale beyond small contexts and works on a token to token basis instead of being hierarchical with sentences, paragraphs and sections. The only models that actually do long context do so by skipping attention layers or doing something without attention or without positional encodings, all leading to shit performance. Nobody pretrains on more than like 8k, except maybe Google who can throw TPUs at the problem.